A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

TitleA multi-task convolutional deep neural network for variant calling in single molecule sequencing.
Publication TypeJournal Article
Year of Publication2019
AuthorsLuo, R, Sedlazeck, FJ, Lam, T-W, Schatz, MC
JournalNat Commun
Volume10
Issue1
Pagination998
Date Published2019 Mar 01
ISSN2041-1723
Abstract

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

DOI10.1038/s41467-019-09025-z
Alternate JournalNat Commun
PubMed ID30824707
PubMed Central IDPMC6397153
Grant ListR01 HG006677 / HG / NHGRI NIH HHS / United States
UM1 HG008898 / HG / NHGRI NIH HHS / United States